Classical computers can keep up with and surpass their quantum counterparts

Boosting speed and accuracy of traditional computing.

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Quantum computing surpasses classical computing in both speed and memory usage. It opens a way to make predictions of physical phenomena which were previously impossible.

Quantum computing is often seen as a paradigm shift from classical or conventional computing. Conventional computers use bits (0 or 1) to process information. On the other side, quantum computers deploy quantum bits (qubits) to store quantum information.

However, quantum computers could be more stable and could retain information. This loss can be avoided, but it is still difficult to translate it into classical information.

Also, Classical computers don’t face the issues of information loss or translation challenges like quantum computers. Additionally, a new study suggests that smartly designed classical algorithms can take advantage of these challenges to imitate the workings of a quantum computer using far fewer resources than previously believed.

The result of this study suggests that classical computing can be reconfigured to perform faster and more accurate calculations. Scientists achieved this breakthrough by using an algorithm that keeps only part of the information stored in the quantum state—and just enough to be able to compute the final outcome accurately.

Dries Sels, an assistant professor in New York University‘s Department of Physics and one of the paper’s authors, said, “This work shows that there are many potential routes to improving computations, encompassing both classical and quantum approaches. Moreover, our work highlights how difficult it is to achieve quantum advantage with an error-prone quantum computer.”

In their quest to enhance classical computing, scientists at the Simons Foundation concentrated on a specific type of tensor network that accurately captures the connections between qubits. These networks have traditionally been challenging to handle, but recent progress in the field has made it possible to optimize them using techniques borrowed from statistical inference.

The Flatiron Institute’s Joseph Tindall, who led the project, said“Choosing different structures for the tensor network corresponds to choosing different forms of compression, like different formats for your image. We are successfully developing tools for working with a wide range of different tensor networks. This work reflects that, and we are confident that we will soon be raising the bar for quantum computing even further.”

Journal Reference:

  1. Joseph Tindall, Matthew Fishman, E. Miles Stoudenmire, and Dries Sels. Efficient Tensor Network Simulation of IBM’s Eagle Kicked Ising Experiment. PRX Quantum. DOI: 10.1103/PRXQuantum.5.010308